Evidence map›Paper›PMID 41425494›Full record

ArticleDrug design, development and therapy2025

Integrating Network Pharmacology and Experimental Validation to Investigate the Action Mechanism of Allicin in Atherosclerosis.

Shuaikai Wu, Tingting Liu, Mingjin Weng, Yuping Zhou, Lijing Ye, Suyan Ruan, Dongmei Tang, Qiong Zhong, Lili Liu, Guojun Zhao

Abstract read
In one paragraph

Article in Drug design, development and therapy, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

Shuaikai WuThe Afiliated Qingyuan Hospital (Qingyuan People's Hospital), Guangzhou Medical University, Qingyuan, Guangdong, People's Republic of China.
Tingting LiuThe Afiliated Qingyuan Hospital (Qingyuan People's Hospital), Guangzhou Medical University, Qingyuan, Guangdong, People's Republic of China.
Mingjin WengSchool of Pharmacy, Zunyi Medical University, Zhuhai, Guangdong, People's Republic of China.
Yuping ZhouCollege of Basic Medical Sciences, Dali University, Dali, Yunnan, People's Republic of China.
Lijing YeThe Afiliated Qingyuan Hospital (Qingyuan People's Hospital), Guangzhou Medical University, Qingyuan, Guangdong, People's Republic of China.
Suyan RuanCollege of Basic Medical Sciences, Dali University, Dali, Yunnan, People's Republic of China.
Dongmei TangSchool of Artificial Intelligence Medicicine, Guilin Medical University, Guilin, Guangxi, People's Republic of China.
Qiong ZhongThe Afiliated Qingyuan Hospital (Qingyuan People's Hospital), Guangzhou Medical University, Qingyuan, Guangdong, People's Republic of China.
Lili LiuThe Afiliated Qingyuan Hospital (Qingyuan People's Hospital), Guangzhou Medical University, Qingyuan, Guangdong, People's Republic of China.
Guojun ZhaoThe Afiliated Qingyuan Hospital (Qingyuan People's Hospital), Guangzhou Medical University, Qingyuan, Guangdong, People's Republic of China.ORCID 0000-0002-9560-2834

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Allicin is a monomer compound derived from traditional Chinese medicine, which has demonstrated significant efficacy in the treatment of cancer, neuroinflammation, gastrointestinal diseases, and other conditions. However, the specific mechanism of action of Allicin in combating cardiovascular diseases remains insufficiently clarified, which limits its application in therapy. Methods: Network pharmacology and molecular docking techniques were employed to explore the potential targets and signaling pathways of Allicin in the treatment of as atherosclerosis (AS). The regulatory effects of Allicin on cell apoptosis, aortic plaques, and lipid levels were assessed through TUNEL staining, Oil Red O staining, HE staining, GPO-PAP, and COD-PAP. Additionally, immunofluorescence assay was conducted to validate the screened key targets. Results: Based on the analysis of network pharmacology and molecular docking techniques, 94 predicted overlapping target genes were identified from the target genes of Allicin and AS-related target genes; Among them, Allicin exhibits a strong binding affinity for five main targets (CASP3, NF-κB1, BTK, MAPK3, and PARP1), and these targets were found to play important role in the anti-apoptotic mechanism of Allicin. Furthermore, Allicin could inhibit the progression of plaques, down- regulating the expressions of CASP3 and NF-κB1, and up-regulate the expressions of BTK, MAPK3, and PARP1 in vivo and in vitro. Conclusion: The present results show that Allicin may improves AS by regulating the main targets of macrophage apoptosis.

Indexed as

AtherosclerosisNetwork PharmacologySulfinic AcidsAnimalsApoptosisDisulfidesDose-Response Relationship, DrugHumansMaleMiceMolecular Docking SimulationMolecular StructureallicinDisulfidesSulfinic Acidsallicinapoptosisatherosclerosisexperimental verificationmolecular docking techniquesnetwork pharmacology

Identifiers

PMID41425494
PMCPMC12717827

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.